The role of school characteristics in pre-legalization cannabis use change among Canadian youth: implications for policy and harm reduction
Bibliographic record
Abstract
Reducing youth cannabis use in Canada is a public health priority with schools of interest as a potential modifier of behavior and as a venue for prevention programming. This work aimed to provide a basis for future policy and programming by evaluating pre-legalization cannabis use change patterns in schools and the impact of school characteristics on these patterns. Average rates of cannabis use behavior change (initiation, escalation, reduction, cessation) were collected from 88 high schools located in Ontario and Alberta, Canada participating in the COMPASS prospective cohort study. There was little variability in cannabis use behaviors between schools with intra-class correlation coefficients lowest for cessation (0.02) and escalation (0.02) followed by initiation (0.03) and reduction (0.05). Modest differences were found based on school province, urbanicity and student-peer use. Cannabis ease of access rates had no significant effect. Fewer than half the schools reported offering school drug use prevention programs; these were not significantly associated with student cannabis use behaviors. In conclusion, current school-based cannabis prevention efforts do not appear sufficiently effective. Comprehensive implementation of universal prevention programs may reduce cannabis harms. Some factors (urbanicity, peer use rates) may indicate which schools to prioritize.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".